A new research paper published on arXiv explores the inherent difficulty in evaluating off-policy performance in partially observable Markov decision processes (POMDPs) when the logging mechanism depends on historical data. The study demonstrates that even with extensive logged data, the ability to accurately assess a target policy's value can be exponentially limited. This limitation arises from the logger's dependence on history, which can obscure crucial transition information, particularly when resets occur. The paper provides a precise characterization of this statistical challenge and proposes an optimal estimator, illustrating the intractability of the problem under specific definitions of revealing behavior. AI
IMPACT Establishes theoretical limits for evaluating AI agents in complex, partially observable environments.
RANK_REASON Academic paper published on arXiv detailing a theoretical finding in reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]
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